Overview

Dataset statistics

Number of variables18
Number of observations35064
Missing cells5277
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory144.0 B

Variable types

Numeric15
Categorical3

Alerts

station has constant value ""Constant
CO is highly overall correlated with NO2 and 2 other fieldsHigh correlation
DEWP is highly overall correlated with PRES and 1 other fieldsHigh correlation
NO2 is highly overall correlated with CO and 3 other fieldsHigh correlation
No is highly overall correlated with yearHigh correlation
O3 is highly overall correlated with NO2 and 1 other fieldsHigh correlation
PM10 is highly overall correlated with CO and 2 other fieldsHigh correlation
PM2.5 is highly overall correlated with CO and 2 other fieldsHigh correlation
PRES is highly overall correlated with DEWP and 1 other fieldsHigh correlation
TEMP is highly overall correlated with DEWP and 2 other fieldsHigh correlation
year is highly overall correlated with NoHigh correlation
PM2.5 has 677 (1.9%) missing valuesMissing
PM10 has 597 (1.7%) missing valuesMissing
SO2 has 1118 (3.2%) missing valuesMissing
NO2 has 744 (2.1%) missing valuesMissing
CO has 1126 (3.2%) missing valuesMissing
O3 has 843 (2.4%) missing valuesMissing
RAIN is highly skewed (γ1 = 27.3358138)Skewed
No is uniformly distributedUniform
No has unique valuesUnique
hour has 1461 (4.2%) zerosZeros
RAIN has 33673 (96.0%) zerosZeros
WSPM has 623 (1.8%) zerosZeros

Reproduction

Analysis started2024-03-08 05:15:26.811117
Analysis finished2024-03-08 05:16:08.863423
Duration42.05 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

No
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct35064
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17532.5
Minimum1
Maximum35064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:16:08.986804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1754.15
Q18766.75
median17532.5
Q326298.25
95-th percentile33310.85
Maximum35064
Range35063
Interquartile range (IQR)17531.5

Descriptive statistics

Standard deviation10122.249
Coefficient of variation (CV)0.57734204
Kurtosis-1.2
Mean17532.5
Median Absolute Deviation (MAD)8766
Skewness0
Sum6.1475958 × 108
Variance1.0245993 × 108
MonotonicityStrictly increasing
2024-03-08T12:16:09.283432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
23379 1
 
< 0.1%
23373 1
 
< 0.1%
23374 1
 
< 0.1%
23375 1
 
< 0.1%
23376 1
 
< 0.1%
23377 1
 
< 0.1%
23378 1
 
< 0.1%
23380 1
 
< 0.1%
23422 1
 
< 0.1%
Other values (35054) 35054
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
35064 1
< 0.1%
35063 1
< 0.1%
35062 1
< 0.1%
35061 1
< 0.1%
35060 1
< 0.1%
35059 1
< 0.1%
35058 1
< 0.1%
35057 1
< 0.1%
35056 1
< 0.1%
35055 1
< 0.1%

year
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
2016
8784 
2014
8760 
2015
8760 
2013
7344 
2017
1416 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters140256
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Length

2024-03-08T12:16:09.677433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:16:09.866168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Most occurring characters

ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140256
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5229295
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:16:10.093657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4487524
Coefficient of variation (CV)0.52871219
Kurtosis-1.2080577
Mean6.5229295
Median Absolute Deviation (MAD)3
Skewness-0.0092942217
Sum228720
Variance11.893893
MonotonicityNot monotonic
2024-03-08T12:16:10.265328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 2976
8.5%
5 2976
8.5%
7 2976
8.5%
8 2976
8.5%
10 2976
8.5%
12 2976
8.5%
1 2976
8.5%
4 2880
8.2%
6 2880
8.2%
9 2880
8.2%
Other values (2) 5592
15.9%
ValueCountFrequency (%)
1 2976
8.5%
2 2712
7.7%
3 2976
8.5%
4 2880
8.2%
5 2976
8.5%
6 2880
8.2%
7 2976
8.5%
8 2976
8.5%
9 2880
8.2%
10 2976
8.5%
ValueCountFrequency (%)
12 2976
8.5%
11 2880
8.2%
10 2976
8.5%
9 2880
8.2%
8 2976
8.5%
7 2976
8.5%
6 2880
8.2%
5 2976
8.5%
4 2880
8.2%
3 2976
8.5%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.729637
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:16:10.561851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8002175
Coefficient of variation (CV)0.55946729
Kurtosis-1.1940295
Mean15.729637
Median Absolute Deviation (MAD)8
Skewness0.0067598056
Sum551544
Variance77.443829
MonotonicityNot monotonic
2024-03-08T12:16:10.985448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1152
 
3.3%
2 1152
 
3.3%
28 1152
 
3.3%
27 1152
 
3.3%
26 1152
 
3.3%
25 1152
 
3.3%
24 1152
 
3.3%
23 1152
 
3.3%
22 1152
 
3.3%
21 1152
 
3.3%
Other values (21) 23544
67.1%
ValueCountFrequency (%)
1 1152
3.3%
2 1152
3.3%
3 1152
3.3%
4 1152
3.3%
5 1152
3.3%
6 1152
3.3%
7 1152
3.3%
8 1152
3.3%
9 1152
3.3%
10 1152
3.3%
ValueCountFrequency (%)
31 672
1.9%
30 1056
3.0%
29 1080
3.1%
28 1152
3.3%
27 1152
3.3%
26 1152
3.3%
25 1152
3.3%
24 1152
3.3%
23 1152
3.3%
22 1152
3.3%

hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros1461
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:16:11.235120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222853
Coefficient of variation (CV)0.60193785
Kurtosis-1.2041745
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum403236
Variance47.918033
MonotonicityNot monotonic
2024-03-08T12:16:11.453934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1461
 
4.2%
1 1461
 
4.2%
22 1461
 
4.2%
21 1461
 
4.2%
20 1461
 
4.2%
19 1461
 
4.2%
18 1461
 
4.2%
17 1461
 
4.2%
16 1461
 
4.2%
15 1461
 
4.2%
Other values (14) 20454
58.3%
ValueCountFrequency (%)
0 1461
4.2%
1 1461
4.2%
2 1461
4.2%
3 1461
4.2%
4 1461
4.2%
5 1461
4.2%
6 1461
4.2%
7 1461
4.2%
8 1461
4.2%
9 1461
4.2%
ValueCountFrequency (%)
23 1461
4.2%
22 1461
4.2%
21 1461
4.2%
20 1461
4.2%
19 1461
4.2%
18 1461
4.2%
17 1461
4.2%
16 1461
4.2%
15 1461
4.2%
14 1461
4.2%

PM2.5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct545
Distinct (%)1.6%
Missing677
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean82.164911
Minimum3
Maximum821
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:16:11.684859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7
Q122
median59
Q3113
95-th percentile243.7
Maximum821
Range818
Interquartile range (IQR)91

Descriptive statistics

Standard deviation80.921384
Coefficient of variation (CV)0.98486547
Kurtosis5.7725514
Mean82.164911
Median Absolute Deviation (MAD)41
Skewness1.9653536
Sum2825404.8
Variance6548.2704
MonotonicityNot monotonic
2024-03-08T12:16:11.972413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 633
 
1.8%
9 577
 
1.6%
11 576
 
1.6%
10 565
 
1.6%
12 542
 
1.5%
8 514
 
1.5%
15 473
 
1.3%
13 469
 
1.3%
7 467
 
1.3%
14 465
 
1.3%
Other values (535) 29106
83.0%
(Missing) 677
 
1.9%
ValueCountFrequency (%)
3 633
1.8%
4 253
 
0.7%
5 299
0.9%
6 405
1.2%
7 467
1.3%
8 514
1.5%
9 577
1.6%
10 565
1.6%
11 576
1.6%
12 542
1.5%
ValueCountFrequency (%)
821 1
< 0.1%
808 1
< 0.1%
801 1
< 0.1%
758 1
< 0.1%
743 1
< 0.1%
720 1
< 0.1%
712 1
< 0.1%
691 1
< 0.1%
671 1
< 0.1%
667 1
< 0.1%

PM10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct640
Distinct (%)1.9%
Missing597
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean106.36367
Minimum2
Maximum988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:16:12.159292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile13
Q141
median85
Q3144
95-th percentile276
Maximum988
Range986
Interquartile range (IQR)103

Descriptive statistics

Standard deviation89.700157
Coefficient of variation (CV)0.84333452
Kurtosis6.4815764
Mean106.36367
Median Absolute Deviation (MAD)49
Skewness1.9414441
Sum3666036.7
Variance8046.1181
MonotonicityNot monotonic
2024-03-08T12:16:12.362918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 302
 
0.9%
24 283
 
0.8%
17 281
 
0.8%
29 280
 
0.8%
18 277
 
0.8%
22 271
 
0.8%
36 269
 
0.8%
20 268
 
0.8%
14 267
 
0.8%
16 266
 
0.8%
Other values (630) 31703
90.4%
(Missing) 597
 
1.7%
ValueCountFrequency (%)
2 6
 
< 0.1%
3 30
 
0.1%
4 16
 
< 0.1%
5 205
0.6%
5.4 2
 
< 0.1%
6 235
0.7%
7 128
0.4%
7.7 1
 
< 0.1%
8 156
0.4%
8.2 1
 
< 0.1%
ValueCountFrequency (%)
988 1
< 0.1%
927 1
< 0.1%
899 1
< 0.1%
894 1
< 0.1%
893 1
< 0.1%
887 1
< 0.1%
870 1
< 0.1%
863 1
< 0.1%
839 2
< 0.1%
807 1
< 0.1%

SO2
Real number (ℝ)

MISSING 

Distinct259
Distinct (%)0.8%
Missing1118
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean14.367615
Minimum0.5712
Maximum273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:16:12.543621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.5712
5-th percentile2
Q13
median7
Q317
95-th percentile55
Maximum273
Range272.4288
Interquartile range (IQR)14

Descriptive statistics

Standard deviation20.144631
Coefficient of variation (CV)1.402086
Kurtosis15.109771
Mean14.367615
Median Absolute Deviation (MAD)5
Skewness3.2605131
Sum487723.06
Variance405.80618
MonotonicityNot monotonic
2024-03-08T12:16:12.779647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 7583
21.6%
3 3251
 
9.3%
4 2253
 
6.4%
5 1779
 
5.1%
6 1687
 
4.8%
7 1388
 
4.0%
8 1209
 
3.4%
9 990
 
2.8%
10 944
 
2.7%
11 802
 
2.3%
Other values (249) 12060
34.4%
(Missing) 1118
 
3.2%
ValueCountFrequency (%)
0.5712 3
 
< 0.1%
0.8568 5
 
< 0.1%
1 234
 
0.7%
1.1424 2
 
< 0.1%
1.428 1
 
< 0.1%
2 7583
21.6%
2.2848 2
 
< 0.1%
2.5704 3
 
< 0.1%
2.856 4
 
< 0.1%
3 3251
9.3%
ValueCountFrequency (%)
273 1
< 0.1%
230 1
< 0.1%
227 2
< 0.1%
215 1
< 0.1%
205 1
< 0.1%
202 1
< 0.1%
200 1
< 0.1%
197 1
< 0.1%
194 1
< 0.1%
193 1
< 0.1%

NO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct319
Distinct (%)0.9%
Missing744
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean53.162646
Minimum2
Maximum241
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:16:12.985427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile14
Q128
median47
Q371
95-th percentile114
Maximum241
Range239
Interquartile range (IQR)43

Descriptive statistics

Standard deviation31.946224
Coefficient of variation (CV)0.60091486
Kurtosis1.4163241
Mean53.162646
Median Absolute Deviation (MAD)21
Skewness1.0748513
Sum1824542
Variance1020.5612
MonotonicityNot monotonic
2024-03-08T12:16:13.216019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 533
 
1.5%
19 533
 
1.5%
22 528
 
1.5%
28 523
 
1.5%
32 516
 
1.5%
23 515
 
1.5%
21 512
 
1.5%
29 501
 
1.4%
31 496
 
1.4%
24 496
 
1.4%
Other values (309) 29167
83.2%
(Missing) 744
 
2.1%
ValueCountFrequency (%)
2 66
0.2%
3 11
 
< 0.1%
4 32
 
0.1%
5 39
 
0.1%
6 54
 
0.2%
7 63
0.2%
8 82
0.2%
9 127
0.4%
10 152
0.4%
10.0597 1
 
< 0.1%
ValueCountFrequency (%)
241 1
< 0.1%
238 1
< 0.1%
236 1
< 0.1%
231 1
< 0.1%
230 1
< 0.1%
228 1
< 0.1%
224 1
< 0.1%
223 1
< 0.1%
221 2
< 0.1%
219 1
< 0.1%

CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct117
Distinct (%)0.3%
Missing1126
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean1298.3033
Minimum100
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:16:13.440142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile300
Q1500
median900
Q31600
95-th percentile3600
Maximum10000
Range9900
Interquartile range (IQR)1100

Descriptive statistics

Standard deviation1170.5933
Coefficient of variation (CV)0.90163314
Kurtosis8.8168753
Mean1298.3033
Median Absolute Deviation (MAD)400
Skewness2.5053371
Sum44061818
Variance1370288.7
MonotonicityNot monotonic
2024-03-08T12:16:13.630237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 2775
 
7.9%
500 2552
 
7.3%
600 2538
 
7.2%
300 2435
 
6.9%
700 2351
 
6.7%
800 2103
 
6.0%
900 1759
 
5.0%
1000 1705
 
4.9%
1100 1522
 
4.3%
1200 1323
 
3.8%
Other values (107) 12875
36.7%
ValueCountFrequency (%)
100 144
 
0.4%
200 670
 
1.9%
300 2435
6.9%
400 2775
7.9%
500 2552
7.3%
600 2538
7.2%
700 2351
6.7%
800 2103
6.0%
900 1759
5.0%
1000 1705
4.9%
ValueCountFrequency (%)
10000 5
< 0.1%
9900 2
 
< 0.1%
9800 1
 
< 0.1%
9700 2
 
< 0.1%
9600 1
 
< 0.1%
9500 1
 
< 0.1%
9300 3
 
< 0.1%
9200 4
< 0.1%
9100 2
 
< 0.1%
9000 8
< 0.1%

O3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct678
Distinct (%)2.0%
Missing843
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean55.984297
Minimum0.4284
Maximum674
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:16:13.830751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.4284
5-th percentile2
Q18
median40
Q381
95-th percentile186
Maximum674
Range673.5716
Interquartile range (IQR)73

Descriptive statistics

Standard deviation59.081528
Coefficient of variation (CV)1.0553232
Kurtosis2.6733922
Mean55.984297
Median Absolute Deviation (MAD)34
Skewness1.5230505
Sum1915838.6
Variance3490.627
MonotonicityNot monotonic
2024-03-08T12:16:14.029141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 4037
 
11.5%
3 1031
 
2.9%
4 768
 
2.2%
5 655
 
1.9%
1 604
 
1.7%
6 554
 
1.6%
8 518
 
1.5%
7 474
 
1.4%
9 415
 
1.2%
11 384
 
1.1%
Other values (668) 24781
70.7%
(Missing) 843
 
2.4%
ValueCountFrequency (%)
0.4284 7
 
< 0.1%
0.6426 13
 
< 0.1%
0.8568 22
 
0.1%
1 604
 
1.7%
1.071 43
 
0.1%
1.2852 42
 
0.1%
1.4994 60
 
0.2%
1.7136 45
 
0.1%
1.9278 40
 
0.1%
2 4037
11.5%
ValueCountFrequency (%)
674 1
 
< 0.1%
673 1
 
< 0.1%
369 1
 
< 0.1%
367 1
 
< 0.1%
360 1
 
< 0.1%
350 1
 
< 0.1%
347 3
< 0.1%
346 1
 
< 0.1%
340 1
 
< 0.1%
332 1
 
< 0.1%

TEMP
Real number (ℝ)

HIGH CORRELATION 

Distinct963
Distinct (%)2.7%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean13.67149
Minimum-16.8
Maximum41.1
Zeros332
Zeros (%)0.9%
Negative5222
Negative (%)14.9%
Memory size274.1 KiB
2024-03-08T12:16:14.241413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-16.8
5-th percentile-4
Q13.1
median14.6
Q323.5
95-th percentile30.7
Maximum41.1
Range57.9
Interquartile range (IQR)20.4

Descriptive statistics

Standard deviation11.458418
Coefficient of variation (CV)0.83812507
Kurtosis-1.1703241
Mean13.67149
Median Absolute Deviation (MAD)9.9
Skewness-0.10086633
Sum479103.68
Variance131.29535
MonotonicityNot monotonic
2024-03-08T12:16:14.715072image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 401
 
1.1%
0 332
 
0.9%
1 326
 
0.9%
-1 315
 
0.9%
2 302
 
0.9%
-2 240
 
0.7%
-4 212
 
0.6%
4 197
 
0.6%
5 196
 
0.6%
-5 193
 
0.6%
Other values (953) 32330
92.2%
ValueCountFrequency (%)
-16.8 2
< 0.1%
-16.3 1
 
< 0.1%
-16.2 1
 
< 0.1%
-16.1 1
 
< 0.1%
-16 1
 
< 0.1%
-15.9 3
< 0.1%
-15.8 2
< 0.1%
-15.6 1
 
< 0.1%
-15.4 1
 
< 0.1%
-15.3 2
< 0.1%
ValueCountFrequency (%)
41.1 1
< 0.1%
40.4 1
< 0.1%
40 1
< 0.1%
39.6 1
< 0.1%
38.8 1
< 0.1%
38.4 1
< 0.1%
38.3 1
< 0.1%
38.2 1
< 0.1%
38.1 1
< 0.1%
38 2
< 0.1%

PRES
Real number (ℝ)

HIGH CORRELATION 

Distinct595
Distinct (%)1.7%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1012.5474
Minimum987.1
Maximum1042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:16:14.978493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum987.1
5-th percentile997
Q11004
median1012.2
Q31020.9
95-th percentile1029.2
Maximum1042
Range54.9
Interquartile range (IQR)16.9

Descriptive statistics

Standard deviation10.266059
Coefficient of variation (CV)0.010138843
Kurtosis-0.9080139
Mean1012.5474
Median Absolute Deviation (MAD)8.4
Skewness0.09963305
Sum35483712
Variance105.39196
MonotonicityNot monotonic
2024-03-08T12:16:15.197381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1023 305
 
0.9%
1021 258
 
0.7%
1025 254
 
0.7%
1024 253
 
0.7%
1022 244
 
0.7%
1020 240
 
0.7%
1026 230
 
0.7%
1014 223
 
0.6%
1019 222
 
0.6%
1016 221
 
0.6%
Other values (585) 32594
93.0%
ValueCountFrequency (%)
987.1 1
 
< 0.1%
987.5 1
 
< 0.1%
987.7 3
< 0.1%
987.8 3
< 0.1%
987.9 1
 
< 0.1%
988.1 1
 
< 0.1%
988.4 1
 
< 0.1%
988.5 1
 
< 0.1%
988.6 2
< 0.1%
988.8 1
 
< 0.1%
ValueCountFrequency (%)
1042 1
 
< 0.1%
1041.8 1
 
< 0.1%
1041.6 1
 
< 0.1%
1041.4 1
 
< 0.1%
1041.2 2
< 0.1%
1041.1 2
< 0.1%
1041 2
< 0.1%
1040.9 1
 
< 0.1%
1040.8 3
< 0.1%
1040.7 1
 
< 0.1%

DEWP
Real number (ℝ)

HIGH CORRELATION 

Distinct617
Distinct (%)1.8%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.4475345
Minimum-35.3
Maximum28.8
Zeros74
Zeros (%)0.2%
Negative15475
Negative (%)44.1%
Memory size274.1 KiB
2024-03-08T12:16:15.438837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-35.3
5-th percentile-20.2
Q1-8.8
median3
Q315
95-th percentile22.1
Maximum28.8
Range64.1
Interquartile range (IQR)23.8

Descriptive statistics

Standard deviation13.810696
Coefficient of variation (CV)5.6426971
Kurtosis-1.1106335
Mean2.4475345
Median Absolute Deviation (MAD)11.9
Skewness-0.19689612
Sum85771.4
Variance190.73532
MonotonicityNot monotonic
2024-03-08T12:16:15.633952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.6 142
 
0.4%
16.9 133
 
0.4%
16.4 130
 
0.4%
16.2 130
 
0.4%
17.2 129
 
0.4%
17 129
 
0.4%
17.8 128
 
0.4%
17.1 128
 
0.4%
17.3 126
 
0.4%
17.7 122
 
0.3%
Other values (607) 33747
96.2%
ValueCountFrequency (%)
-35.3 1
< 0.1%
-35.1 1
< 0.1%
-35 1
< 0.1%
-34.8 1
< 0.1%
-34.5 1
< 0.1%
-34.3 2
< 0.1%
-34.2 1
< 0.1%
-34.1 1
< 0.1%
-33.8 1
< 0.1%
-33.7 1
< 0.1%
ValueCountFrequency (%)
28.8 2
< 0.1%
28.7 3
< 0.1%
28.5 2
< 0.1%
28.4 4
< 0.1%
28.3 2
< 0.1%
28.2 3
< 0.1%
28.1 3
< 0.1%
28 1
 
< 0.1%
27.9 1
 
< 0.1%
27.8 4
< 0.1%

RAIN
Real number (ℝ)

SKEWED  ZEROS 

Distinct119
Distinct (%)0.3%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.064019518
Minimum0
Maximum46.4
Zeros33673
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:16:15.839906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum46.4
Range46.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.78628199
Coefficient of variation (CV)12.28191
Kurtosis1020.5059
Mean0.064019518
Median Absolute Deviation (MAD)0
Skewness27.335814
Sum2243.5
Variance0.61823937
MonotonicityNot monotonic
2024-03-08T12:16:16.089969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33673
96.0%
0.1 310
 
0.9%
0.2 147
 
0.4%
0.3 118
 
0.3%
0.5 74
 
0.2%
0.4 71
 
0.2%
0.6 57
 
0.2%
0.7 47
 
0.1%
0.9 41
 
0.1%
0.8 35
 
0.1%
Other values (109) 471
 
1.3%
ValueCountFrequency (%)
0 33673
96.0%
0.1 310
 
0.9%
0.2 147
 
0.4%
0.3 118
 
0.3%
0.4 71
 
0.2%
0.5 74
 
0.2%
0.6 57
 
0.2%
0.7 47
 
0.1%
0.8 35
 
0.1%
0.9 41
 
0.1%
ValueCountFrequency (%)
46.4 1
< 0.1%
36.6 1
< 0.1%
33.7 1
< 0.1%
33.1 1
< 0.1%
29.3 1
< 0.1%
29 1
< 0.1%
27.3 1
< 0.1%
24.1 1
< 0.1%
23.7 1
< 0.1%
21.7 1
< 0.1%

wd
Categorical

Distinct16
Distinct (%)< 0.1%
Missing78
Missing (%)0.2%
Memory size274.1 KiB
ENE
3861 
E
3564 
NE
3540 
ESE
2706 
SW
2481 
Other values (11)
18834 

Length

Max length3
Median length2
Mean length2.2486423
Min length1

Characters and Unicode

Total characters78671
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNNW
2nd rowNW
3rd rowNNW
4th rowN
5th rowNNW

Common Values

ValueCountFrequency (%)
ENE 3861
11.0%
E 3564
10.2%
NE 3540
10.1%
ESE 2706
 
7.7%
SW 2481
 
7.1%
NW 2466
 
7.0%
SSW 1953
 
5.6%
NNE 1928
 
5.5%
SE 1880
 
5.4%
N 1865
 
5.3%
Other values (6) 8742
24.9%

Length

2024-03-08T12:16:16.355464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ene 3861
11.0%
e 3564
10.2%
ne 3540
10.1%
ese 2706
 
7.7%
sw 2481
 
7.1%
nw 2466
 
7.0%
ssw 1953
 
5.6%
nne 1928
 
5.5%
se 1880
 
5.4%
n 1865
 
5.3%
Other values (6) 8742
25.0%

Most occurring characters

ValueCountFrequency (%)
E 25448
32.3%
N 20321
25.8%
S 17093
21.7%
W 15809
20.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 78671
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 25448
32.3%
N 20321
25.8%
S 17093
21.7%
W 15809
20.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 78671
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 25448
32.3%
N 20321
25.8%
S 17093
21.7%
W 15809
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78671
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 25448
32.3%
N 20321
25.8%
S 17093
21.7%
W 15809
20.1%

WSPM
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)0.3%
Missing14
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.8607846
Minimum0
Maximum10.5
Zeros623
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:16:16.634194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q11
median1.5
Q32.4
95-th percentile4.455
Maximum10.5
Range10.5
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.2803683
Coefficient of variation (CV)0.68807978
Kurtosis3.3696482
Mean1.8607846
Median Absolute Deviation (MAD)0.6
Skewness1.566528
Sum65220.5
Variance1.6393429
MonotonicityNot monotonic
2024-03-08T12:16:16.836127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 1863
 
5.3%
1.1 1838
 
5.2%
1 1782
 
5.1%
1.3 1713
 
4.9%
0.9 1642
 
4.7%
1.4 1572
 
4.5%
1.5 1427
 
4.1%
0.8 1385
 
3.9%
1.6 1324
 
3.8%
0.7 1269
 
3.6%
Other values (91) 19235
54.9%
ValueCountFrequency (%)
0 623
 
1.8%
0.1 234
 
0.7%
0.2 280
 
0.8%
0.3 244
 
0.7%
0.4 473
 
1.3%
0.5 691
2.0%
0.6 956
2.7%
0.7 1269
3.6%
0.8 1385
3.9%
0.9 1642
4.7%
ValueCountFrequency (%)
10.5 1
 
< 0.1%
10.3 1
 
< 0.1%
10.2 1
 
< 0.1%
9.9 2
< 0.1%
9.8 1
 
< 0.1%
9.7 4
< 0.1%
9.6 1
 
< 0.1%
9.5 1
 
< 0.1%
9.3 1
 
< 0.1%
9.2 2
< 0.1%

station
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
Tiantan
35064 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters245448
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTiantan
2nd rowTiantan
3rd rowTiantan
4th rowTiantan
5th rowTiantan

Common Values

ValueCountFrequency (%)
Tiantan 35064
100.0%

Length

2024-03-08T12:16:17.077759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:16:17.207391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
tiantan 35064
100.0%

Most occurring characters

ValueCountFrequency (%)
a 70128
28.6%
n 70128
28.6%
T 35064
14.3%
i 35064
14.3%
t 35064
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 210384
85.7%
Uppercase Letter 35064
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 70128
33.3%
n 70128
33.3%
i 35064
16.7%
t 35064
16.7%
Uppercase Letter
ValueCountFrequency (%)
T 35064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 245448
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 70128
28.6%
n 70128
28.6%
T 35064
14.3%
i 35064
14.3%
t 35064
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 245448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 70128
28.6%
n 70128
28.6%
T 35064
14.3%
i 35064
14.3%
t 35064
14.3%

Interactions

2024-03-08T12:16:04.415403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:28.600663image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:31.669110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:33.753353image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:35.920359image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:38.229433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:40.879021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:43.344603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:45.737871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:48.161207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:51.051912image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:54.100574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:56.605316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:59.443358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:01.765622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:04.645247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:28.752374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:31.880674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:33.927229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:36.059103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:38.381396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:41.067061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:43.498204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:45.861103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:48.317321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:51.262152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:54.291070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:56.818720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:59.649987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:01.983429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:04.820163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:28.886114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:31.991390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:34.055563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:36.222498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:38.572922image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:41.235488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:43.639381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:45.987507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:48.455030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:51.493596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:54.459211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:56.973849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:59.816471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:02.189619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:04.955777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:29.083186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:32.175242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:34.221354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:36.381514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:38.773235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:41.381920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:43.790359image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:46.151052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:48.850482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:51.686398image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:54.632951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:57.159746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:59.962214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:02.379850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:05.164377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:29.319569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:32.292521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:34.401327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:36.520164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:38.903481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:41.594058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:43.951326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:46.281940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:48.997497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:51.873262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:54.822279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:57.392688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:00.110340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:02.497288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:05.396477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:29.497964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:32.414012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:34.558712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:36.641843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:39.039227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:41.774040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:44.108625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:46.456955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:49.142460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:52.181108image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:54.975204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:57.563701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:00.278072image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:02.744364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:05.654066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:29.664854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:32.532928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:34.700773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:36.839919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:39.175637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:41.941589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:44.241839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:46.665021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:49.266112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:52.363560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:55.133183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:57.730623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:00.479633image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:02.932969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:05.836412image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:29.854326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:32.658312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:34.858704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:36.969386image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:39.319974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:42.113944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:44.386101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:46.830778image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:49.391629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:52.558202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:55.308555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:57.879290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:00.624154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:03.063678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:05.997452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:30.011552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:32.791790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:34.979883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:37.141223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:39.760528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:42.262261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:44.593985image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:46.959876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:49.566362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:52.781349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:55.445319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:58.012150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:00.757642image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:03.212194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:06.210124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:30.163128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:32.920959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:35.108045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:37.311179image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:39.900078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:42.405674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:44.755900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:47.125536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:49.725859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:52.990987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:55.582793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:58.154348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:00.883855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:03.354244image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:06.380411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:30.349739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:33.065532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:35.238695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:37.445551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:40.086011image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:42.590945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:44.959556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:47.279977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:49.910824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:53.171190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:55.761732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:58.295317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:01.033015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:03.545699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:06.522878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:30.550448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:33.188932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:35.378208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:37.611040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:40.205834image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:42.765161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:45.085680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:47.432574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:50.061808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:53.355516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:55.926836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:58.479016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:01.157188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:03.693822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:06.762856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:30.723940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:33.318893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:35.531320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:37.781117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:40.339984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:42.907868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:45.241188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:47.615190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:50.288643image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:53.549497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:56.077504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:58.892177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:01.326241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:03.883196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:06.984026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:30.903075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:33.450324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:35.657183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:37.953137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:40.514025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:43.046141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:45.420166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:47.821432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:50.665016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:53.746477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:56.280609image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:59.072420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:01.502797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:04.012522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:07.152389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:31.446125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:33.583759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:35.782996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:38.099309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:40.695794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:43.193783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:45.567066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:48.023933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:50.866990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:53.915589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:56.413336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:59.227650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:01.637579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:04.230959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-03-08T12:16:17.338416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CODEWPNO2NoO3PM10PM2.5PRESRAINSO2TEMPWSPMdayhourmonthwdyear
CO1.0000.1310.716-0.044-0.4730.7300.8510.0690.0160.453-0.206-0.4420.007-0.0600.0670.1160.080
DEWP0.1311.000-0.072-0.0890.2030.1430.246-0.7750.178-0.3000.815-0.2090.019-0.0210.2560.1220.148
NO20.716-0.0721.000-0.024-0.6690.5560.5980.199-0.0650.380-0.327-0.4790.020-0.0720.0630.1160.069
No-0.044-0.089-0.0241.000-0.053-0.084-0.0640.1670.000-0.245-0.1150.0130.0180.0010.0440.0850.862
O3-0.4730.203-0.669-0.0531.000-0.210-0.270-0.406-0.005-0.0030.5590.498-0.0090.291-0.2120.1730.043
PM100.7300.1430.556-0.084-0.2101.0000.886-0.092-0.0830.436-0.032-0.2570.0300.044-0.0160.0860.072
PM2.50.8510.2460.598-0.064-0.2700.8861.000-0.089-0.0220.430-0.035-0.3550.014-0.0130.0150.0940.058
PRES0.069-0.7750.1990.167-0.406-0.092-0.0891.000-0.0870.210-0.8410.0000.010-0.037-0.0110.0790.148
RAIN0.0160.178-0.0650.000-0.005-0.083-0.022-0.0871.000-0.1530.039-0.005-0.010-0.0070.0420.0050.010
SO20.453-0.3000.380-0.245-0.0030.4360.4300.210-0.1531.000-0.2500.0440.0120.083-0.2530.0490.099
TEMP-0.2060.815-0.327-0.1150.559-0.032-0.035-0.8410.039-0.2501.0000.1300.0180.1460.1260.1100.148
WSPM-0.442-0.209-0.4790.0130.498-0.257-0.3550.000-0.0050.0440.1301.0000.0030.181-0.1520.1810.042
day0.0070.0190.0200.018-0.0090.0300.0140.010-0.0100.0120.0180.0031.0000.0000.0100.0310.000
hour-0.060-0.021-0.0720.0010.2910.044-0.013-0.037-0.0070.0830.1460.1810.0001.0000.0000.1280.000
month0.0670.2560.0630.044-0.212-0.0160.015-0.0110.042-0.2530.126-0.1520.0100.0001.0000.0850.249
wd0.1160.1220.1160.0850.1730.0860.0940.0790.0050.0490.1100.1810.0310.1280.0851.0000.089
year0.0800.1480.0690.8620.0430.0720.0580.1480.0100.0990.1480.0420.0000.0000.2490.0891.000

Missing values

2024-03-08T12:16:07.429004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-08T12:16:07.937951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-08T12:16:08.633281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
0120133106.06.04.08.0300.081.0-0.51024.5-21.40.0NNW5.7Tiantan
1220133116.029.05.09.0300.080.0-0.71025.1-22.10.0NW3.9Tiantan
2320133126.06.04.012.0300.075.0-1.21025.3-24.60.0NNW5.3Tiantan
3420133136.06.04.012.0300.074.0-1.41026.2-25.50.0N4.9Tiantan
4520133145.05.07.015.0400.070.0-1.91027.1-24.50.0NNW3.2Tiantan
56201331510.010.012.015.0400.070.0-2.41027.5-21.30.0NW2.4Tiantan
6720133168.019.012.014.0400.072.0-2.51028.2-20.40.0NW2.2Tiantan
7820133177.07.012.019.0400.067.0-1.41029.5-20.40.0NNW3.0Tiantan
8920133183.06.014.029.0500.056.0-0.31030.4-21.20.0NW4.6Tiantan
91020133198.02.011.022.0500.065.00.41030.5-23.30.0N5.5Tiantan
NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
350543505520172281413.049.02.019.0300.091.014.61013.3-15.60.0N3.6Tiantan
35055350562017228159.09.02.022.0300.091.015.41013.0-15.00.0NNW3.3Tiantan
350563505720172281610.029.03.021.0300.094.014.91012.6-15.40.0NW2.1Tiantan
35057350582017228179.037.0NaNNaNNaNNaN14.21012.5-14.90.0NW3.1Tiantan
350583505920172281815.043.0NaNNaNNaNNaN13.41013.0-15.50.0WNW1.4Tiantan
350593506020172281920.048.02.0NaN500.0NaN12.51013.5-16.20.0NW2.4Tiantan
350603506120172282011.034.03.036.0500.0NaN11.61013.6-15.10.0WNW0.9Tiantan
350613506220172282118.032.04.048.0500.048.010.81014.2-13.30.0NW1.1Tiantan
350623506320172282215.042.05.052.0600.044.010.51014.4-12.90.0NNW1.2Tiantan
350633506420172282315.050.05.068.0700.021.08.61014.1-15.90.0NNE1.3Tiantan